CN112241604A - Probability matching series-parallel coupling multi-model power grid rainstorm disaster forecast correction method - Google Patents
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Abstract
The invention relates to the technical field of power grid disaster forecast, and discloses a power grid rainstorm disaster forecast correcting method with probability matching series-parallel coupling multiple models, so that the accuracy of power grid rainstorm disaster forecast is improved. The method comprises the following steps: selecting a forecasting area, and acquiring actual rainfall and power grid rainstorm disaster information in the historical period of the area to establish at least more than two power grid rainstorm forecasting models for forecasting the rainfall of the area; correspondingly correcting the precipitation of each power grid rainstorm forecasting model by using a probability matching correction method; predicting error sequence e at t moment by adopting error autoregressive model for each power grid rainstorm forecasting modeltAnd serially correcting the models to obtain a precipitation sequence; performing parallel correction of the plurality of models in S4 by using a least square method; using particle swarm intelligent optimization algorithmThe method comprises the steps of respectively solving parameters of series correction and parallel correction, establishing an integrated coupling power grid rainstorm forecasting correction model, and calculating a corrected result.
Description
Technical Field
The invention relates to the technical field of power grid disaster forecast, in particular to a power grid rainstorm disaster forecast correcting method of a probability matching series-parallel connection coupling multi-model, namely: a probability matching correction method for forecasting power grid rainstorm disasters by series-parallel coupling multiple models is disclosed.
Background
In recent years, rainstorm natural disasters cause great harm to domestic and foreign power grids, and huge loss is brought to the society. 7 months in 2012, when meeting very strong rainfall weather in 500 years coming in Beijing City, the average rainfall capacity in urban areas is 215mm, and the urban area is 460mm in the river north town, so that all 110kV magnetic household substations in the areas are powered off. According to statistics, the '7.21' rainstorm disaster impacts a Beijing power grid, instantaneous faults occur at 220kV and 110kV, the power grid is influenced by mountain torrents and accumulated water, 76 permanent faults occur in 10kV equipment of the power grid, 1 permanent fault occurs in 35kV equipment of the power grid, and the rainstorm disaster causes great damage to a power transmission network and a power distribution network. Although rainfall forecast is routinely carried out by meteorological departments, the phenomenon of large deviation of rainstorm forecast is inevitable. Improving the accuracy of power grid rainstorm forecasting is important content of disaster prevention and reduction of power transmission and transformation equipment, and it is necessary to research the power grid rainstorm accurate forecasting technology by the current scientific and effective means. Therefore, it is highly desirable to develop power grid rainstorm disaster forecast correction work.
The current method for improving the rainstorm forecasting precision mainly comprises two methods of real-time correction and combined forecasting. The real-time correction method predicts the error value at the future time by using the correlation characteristics of the prediction error sequence, and further realizes the real-time correction of rainstorm prediction, for example, the method of "preferred prescription of real-time correction model in flood prediction system" disclosed in representative patent CN 201010106038.9. The combined forecasting method is to use the complementarity of various current forecasting models (including but not limited to numerical model forecasting, statistical analysis forecasting and the like), integrate the idea of weighting optimization among the models, select the centralized forecasting models of different forecasting regions to establish the combined forecasting method, and further realize the correction of rainstorm forecasting, such as the hydrological forecasting method of combining hydrological models of different mechanisms disclosed in the representative patent CN 200910234628.7.
Meanwhile, patent CN201510683497.6 discloses a serial-parallel coupled multi-model hydrological prediction method, which takes advantages of two traditional methods, namely real-time correction and combined prediction into full play, and establishes a serial-parallel coupled hydrological prediction model, including a serial-to-parallel, parallel-to-serial and integrated coupling method, thereby reducing prediction errors to the maximum and improving prediction accuracy. However, the uncertainty of the initial field of the rainstorm numerical prediction mode, the imperfection of the parameterization method and the chaos characteristic of the atmosphere, so that a prediction deviation may be large due to some small phase errors in the mode, and the rainstorm prediction statistical method fails to fully consider the influence mechanism factors of rainstorm to cause that the rainstorm prediction magnitude of a local area is far from each other.
Aiming at the problems of the method, a power grid rainstorm disaster forecasting and correcting method with stronger forecasting error adjusting capability and wider application range is urgently needed to reduce the rainstorm and secondary disaster loss of the power transmission line and improve the capability of the power transmission line for dealing with the rainstorm disaster and the safe and stable operation level.
Disclosure of Invention
The invention mainly aims to disclose a probability matching series-parallel coupling multi-model power grid rainstorm disaster forecast correcting method so as to improve the accuracy of power grid rainstorm disaster forecast.
In order to achieve the aim, the invention discloses a probability matching series-parallel coupling multi-model power grid rainstorm disaster forecast correcting method, which comprises the following steps of:
step S1, selecting a forecasting area, and acquiring actual rainfall and power grid rainstorm disaster information in the historical period of the area to establish at least more than two power grid rainstorm forecasting models for forecasting the rainfall of the area;
step S2, respectively recording the number of the power grid rainstorm forecasting models as n and the measured rainfall sequence at the time t as M according to the measured rainfall in the forecasting area and the rainfall of each power grid rainstorm forecasting modeltThe rainfall result of each power grid rainstorm forecasting model is { Mit, i ═ 1., n };
step S3, the probability matching correction method is used to correspondingly correct the rainfall of each power grid rainstorm forecasting model, and the result is recorded as { Mit*,i＝1,...,n}；
Step S4, adopting an error autoregressive model to predict an error sequence e at the time t for each power grid rainstorm forecasting modeltAnd obtaining a precipitation sequence { Mit ] through serial correction of the models*^,i＝1,...,n}；
Step S5, performing parallel correction of the plurality of models in S4 by using a least square method;
and step S6, respectively solving parameters of series correction and parallel correction by adopting a particle swarm intelligent optimization algorithm, establishing an integrated coupling power grid rainstorm forecast correction model, and calculating a corrected result.
The invention has the following beneficial effects:
on one hand, the power grid rainstorm numerical model forecasting model is considered, historical rainstorm disaster information of the power grid is fully utilized, the pre-correction rainfall forecasting model based on probability matching is established, and the accuracy of power grid rainstorm disaster forecasting is improved.
On the other hand, the probability matching series-parallel coupling-based multi-model power grid rainstorm disaster forecast correction method is adopted, the ability of being suitable for new data is favorably strengthened in the rules learned from sample data, the method has the characteristics of strong generalization ability, easiness in coding and the like, and the reliability of the prediction early warning result is high.
In addition, the method has the advantages of relatively detailed flow, strong operability and high practicability.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a power grid rainstorm disaster forecast correction method of a probability matching series-parallel connection coupling multi-model according to an embodiment of the invention.
Fig. 2 is a schematic diagram of probability distribution correction according to an embodiment of the present invention. Wherein, the approximate straight line represents the forecasted accumulated probability data, and the other curve similar to the obvious two-fold line is the actually measured accumulated probability data.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
The embodiment discloses a probability matching series-parallel coupling multi-model power grid rainstorm disaster forecast correction method, as shown in fig. 1, including:
and step S1, establishing a plurality of power grid rainstorm forecasting models.
The method comprises the following steps: selecting a forecasting area, and acquiring actual rainfall and power grid rainstorm disaster information in the historical period of the area to establish at least more than two power grid rainstorm forecasting models for forecasting the rainfall of the area.
Step S2, respectively recording the number of the power grid rainstorm forecasting models as n and the measured rainfall sequence at the time t as M according to the measured rainfall in the forecasting area and the rainfall of each power grid rainstorm forecasting modeltAnd the precipitation result of each power grid rainstorm forecasting model is { Mit, i ═ 1,. once, n }.
Step S3, the probability matching correction method is used to correspondingly correct the rainfall of each power grid rainstorm forecasting model, and the result is recorded as { Mit*,i＝1,...,n}。
Preferably, the steps specifically include:
obtaining the cumulative probability value P (X) of the actually measured rainfall according to the statistical result of the actually measured and forecast historical datai)。
Using the cumulative probability distribution function of the actually measured precipitation, and according to the consistency of the cumulative probability distribution of the observation and the cumulative probability distribution of the forecast model, P (X)i’)＝P(Xi) Solving the power grid rainstorm precipitation predicted value Xi’。
And (3) calculating the reserved positive precipitation after probability matching, wherein the formula is as follows:
wherein M isit，Mit *Respectively representing the precipitation magnitude before and after the model forecast is corrected, ynThe correction threshold corresponding to the nth accumulated precipitation is selected.
Referring to the schematic diagram of probability distribution correction in fig. 2, the above steps are pre-corrected by using a probability matching method.
Step S4, adopting an error autoregressive model to predict an error sequence e at the time t for each power grid rainstorm forecasting modeltAnd obtaining a precipitation sequence { Mit ] through serial correction of the models*^,i＝1,...,n}。
In this step, the error sequence etThe calculation formula is as follows:
wherein, thetai(i ═ 1, 2.,. q) are parameters of the autoregressive model, and ξ is the absolute error. And (3) establishing a q-order autoregressive model AR (q) according to the formula, and determining the order of the model by using an AIC (advanced information center) criterion in order that the selected autoregressive model has the best data simulation effect. The aic (q) values of the q-order autoregressive model ar (q) and the tandem model can be found in patent CN 201510683497.6.
In step S5, parallel correction of the plurality of models in S4 is performed by the least square method.
In this step, a calculation formula of a parallel correction model is established as follows:
Gt＝ω1M1t *^+ω2M2t *^+...+ωnMnt *^
wherein, ω isiCoupling weight of the ith power grid rainstorm forecast model and meeting conditions
In a simulation calculation of the present embodiment, data of coupling weights obtained for 5 power grid rainstorm forecasting models are shown in table 1 below.
Table 1:
| serial number | Precipitation observation value (mm) | Precipitation prediction value (mm) | Weight of |
| 1 | 55 | 38 | 1/18 |
| 2 | 55 | 50 | 5/18 |
| 3 | 55 | 52 | 1/4 |
| 4 | 55 | 58 | 1/4 |
| 5 | 55 | 65 | 3/18 |
And step S6, respectively solving parameters of series correction and parallel correction by adopting a particle swarm intelligent optimization algorithm, establishing an integrated coupling power grid rainstorm forecast correction model, and calculating a corrected result.
Preferably, in this step, the established integrated coupling grid rainstorm forecast correction model is as follows:
wherein, the result of the parallel correction of the area at the time t is Gt，E(Mt-Gt)2Is an error expectation.
To sum up, the power grid rainstorm disaster forecast correction method with probability matching series-parallel coupling multi-model disclosed by the embodiment of the invention has the following beneficial effects:
on one hand, the power grid rainstorm numerical model forecasting model is considered, historical rainstorm disaster information of the power grid is fully utilized, the pre-correction rainfall forecasting model based on probability matching is established, and the accuracy of power grid rainstorm disaster forecasting is improved.
On the other hand, the probability matching series-parallel coupling-based multi-model power grid rainstorm disaster forecast correction method is adopted, the ability of being suitable for new data is favorably strengthened in the rules learned from sample data, the method has the characteristics of strong generalization ability, easiness in coding and the like, and the reliability of the prediction early warning result is high.
In addition, the method has the advantages of relatively detailed flow, strong operability and high practicability.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A probability matching series-parallel connection coupling multi-model power grid rainstorm disaster forecast correction method is characterized by comprising the following steps:
step S1, selecting a forecasting area, and acquiring actual rainfall and power grid rainstorm disaster information in the historical period of the area to establish at least more than two power grid rainstorm forecasting models for forecasting the rainfall of the area;
step S2, respectively recording the number of the power grid rainstorm forecasting models as n and the measured rainfall sequence at the time t as M according to the measured rainfall in the forecasting area and the rainfall of each power grid rainstorm forecasting modeltThe rainfall result of each power grid rainstorm forecasting model is { Mit, i ═ 1., n };
step S3, the probability matching correction method is used to carry out corresponding correction on the rainfall of each power grid rainstorm forecasting modelThe correction result is expressed as { Mit*,i＝1,...,n}；
Step S4, adopting an error autoregressive model to predict an error sequence e at the time t for each power grid rainstorm forecasting modeltAnd obtaining a precipitation sequence { Mit ] through serial correction of the models*^,i＝1,...,n}；
Step S5, performing parallel correction of the plurality of models in S4 by using a least square method;
and step S6, respectively solving parameters of series correction and parallel correction by adopting a particle swarm intelligent optimization algorithm, establishing an integrated coupling power grid rainstorm forecast correction model, and calculating a corrected result.
2. The probabilistic matching series-parallel coupling multi-model power grid rainstorm disaster forecast correction method according to claim 1, wherein the step S3 specifically comprises:
obtaining the cumulative probability value P (X) of the actually measured rainfall according to the statistical result of the actually measured and forecast historical datai)；
Using the cumulative probability distribution function of the actually measured precipitation, and according to the consistency of the cumulative probability distribution of the observation and the cumulative probability distribution of the forecast model, P (X)i’)＝P(Xi) Solving the power grid rainstorm precipitation predicted value Xi’；
And (3) calculating the reserved positive precipitation after probability matching, wherein the formula is as follows:
wherein M isit，Mit *Respectively representing the precipitation magnitude before and after the model forecast is corrected, ynThe correction threshold corresponding to the nth accumulated precipitation is selected.
3. The probability matching series-parallel connection coupling multi-model power grid rainstorm disaster forecast correction method as claimed in claim 1 or 2, wherein the error sequence e in step S4tThe calculation formula is as follows:
wherein, thetaiThe parameters of the autoregressive model are i 1,2,.,. q, xi are absolute errors; in step S5, a calculation formula of the parallel correction model is established as follows:
Gt＝ω1M1t *^+ω2M2t *^+...+ωnMnt *^
wherein, ω isiCoupling weight of the ith power grid rainstorm forecasting model; step S6, an integrated coupling power grid rainstorm forecast correction model is established as follows:
wherein, the result of the parallel correction of the area at the time t is Gt，E(Mt-Gt)2Is an error expectation.
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